Prediction of coalbed methane productivity based on neural network models
ObjectivesThe productivity of coalbed methane is mainly affected by geological and engineering factors. Clarifying the influence mechanism of these factors on the productivity of coalbed methane wells is the basis for achieving fine reservoir reconstruction and increasing production of coalbed metha...
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Academic Publishing Center of HPU
2025-01-01
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| Series: | 河南理工大学学报. 自然科学版 |
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| Online Access: | http://xuebao.hpu.edu.cn/info/11196/95996.htm |
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| author | JIN Yi ZHENG Chenhui SONG Huibo MA Jiaheng YANG Yunhang LIU Shunxi ZHANG Kun NI Xiaoming |
| author_facet | JIN Yi ZHENG Chenhui SONG Huibo MA Jiaheng YANG Yunhang LIU Shunxi ZHANG Kun NI Xiaoming |
| author_sort | JIN Yi |
| collection | DOAJ |
| description | ObjectivesThe productivity of coalbed methane is mainly affected by geological and engineering factors. Clarifying the influence mechanism of these factors on the productivity of coalbed methane wells is the basis for achieving fine reservoir reconstruction and increasing production of coalbed methane wells.MethodsTherefore， this paper takes Shizhuang South Block in Qinshui Basin as the research object， and comprehensively considers the geological background， reservoir physical properties and dynamic drainage data， uses neural network algorithm to carry out CBM productivity prediction. Firstly， 10 geological parameters were selected as the main controlling factors for CBM productivity prediction by grey correlation analysis. On this basis， the fuzzy mathematics method was used to realize the division of 34 coalbed methane wells in the study area. Finally， according to the classification results， combined with the actual drainage data， the BP and LSTM neural network algorithms were used to predict the daily gas production of CBM wells.ResultsThe results show that： （1） Based on the grey correlation method model analysis， 10 parameters such as permeability， gas saturation and reservoir pressure gradient in the study area are the key factors affecting the gas production performance of coalbed methane； （2） Using fuzzy mathematics evaluation method to evaluate the enrichment of coalbed methane， the gas production effects of 34 wells in the study area is divided into three categories： favorable area， relatively favorable area and unfavorable area. （3）A coal reservoir daily gas production prediction model was established based on the LSTM algorithm， with a prediction error value between 4.06% and 14.79%， and the average error value of 11.09%. The prediction accuracy is significantly higher than the BP model.ConclusionsThe model has good stability and high prediction accuracy. It can be used as an effective means for long-term prediction of coal reservoir producti-vity， and then provide scientific basis for deployment of coalbed methane development processes and the formulation of procurement plans. the formulation of coalbed methane development plan and the scientific deployment of drainage technology. |
| format | Article |
| id | doaj-art-cd25c9d2b77f4e7b813e935e6d0cab8e |
| institution | OA Journals |
| issn | 1673-9787 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Academic Publishing Center of HPU |
| record_format | Article |
| series | 河南理工大学学报. 自然科学版 |
| spelling | doaj-art-cd25c9d2b77f4e7b813e935e6d0cab8e2025-08-20T02:26:04ZzhoAcademic Publishing Center of HPU河南理工大学学报. 自然科学版1673-97872025-01-01441465610.16186/j.cnki.1673-9787.20230300831673-9787（2025）1-46-11Prediction of coalbed methane productivity based on neural network modelsJIN Yi0ZHENG Chenhui1SONG Huibo2MA Jiaheng3YANG Yunhang4LIU Shunxi5ZHANG Kun6NI Xiaoming7School of Resources and Environment， Henan Polytechnic University， Jiaozuo 454000， Henan， ChinaSchool of Resources and Environment， Henan Polytechnic University， Jiaozuo 454000， Henan， ChinaSchool of Resources and Environment， Henan Polytechnic University， Jiaozuo 454000， Henan， ChinaSchool of Resources and Environment， Henan Polytechnic University， Jiaozuo 454000， Henan， ChinaSchool of Resources and Environment， Henan Polytechnic University， Jiaozuo 454000， Henan， ChinaSchool of Resources and Environment， Henan Polytechnic University， Jiaozuo 454000， Henan， ChinaSchool of Resources and Environment， Henan Polytechnic University， Jiaozuo 454000， Henan， ChinaSchool of Energy Science and Engineering， Henan Polytechnic University， Jiaozuo 454000， Henan， ChinaObjectivesThe productivity of coalbed methane is mainly affected by geological and engineering factors. Clarifying the influence mechanism of these factors on the productivity of coalbed methane wells is the basis for achieving fine reservoir reconstruction and increasing production of coalbed methane wells.MethodsTherefore， this paper takes Shizhuang South Block in Qinshui Basin as the research object， and comprehensively considers the geological background， reservoir physical properties and dynamic drainage data， uses neural network algorithm to carry out CBM productivity prediction. Firstly， 10 geological parameters were selected as the main controlling factors for CBM productivity prediction by grey correlation analysis. On this basis， the fuzzy mathematics method was used to realize the division of 34 coalbed methane wells in the study area. Finally， according to the classification results， combined with the actual drainage data， the BP and LSTM neural network algorithms were used to predict the daily gas production of CBM wells.ResultsThe results show that： （1） Based on the grey correlation method model analysis， 10 parameters such as permeability， gas saturation and reservoir pressure gradient in the study area are the key factors affecting the gas production performance of coalbed methane； （2） Using fuzzy mathematics evaluation method to evaluate the enrichment of coalbed methane， the gas production effects of 34 wells in the study area is divided into three categories： favorable area， relatively favorable area and unfavorable area. （3）A coal reservoir daily gas production prediction model was established based on the LSTM algorithm， with a prediction error value between 4.06% and 14.79%， and the average error value of 11.09%. The prediction accuracy is significantly higher than the BP model.ConclusionsThe model has good stability and high prediction accuracy. It can be used as an effective means for long-term prediction of coal reservoir producti-vity， and then provide scientific basis for deployment of coalbed methane development processes and the formulation of procurement plans. the formulation of coalbed methane development plan and the scientific deployment of drainage technology.http://xuebao.hpu.edu.cn/info/11196/95996.htmlstm neural networkbp neural networkgrey correlation analysisproductivity prediction |
| spellingShingle | JIN Yi ZHENG Chenhui SONG Huibo MA Jiaheng YANG Yunhang LIU Shunxi ZHANG Kun NI Xiaoming Prediction of coalbed methane productivity based on neural network models 河南理工大学学报. 自然科学版 lstm neural network bp neural network grey correlation analysis productivity prediction |
| title | Prediction of coalbed methane productivity based on neural network models |
| title_full | Prediction of coalbed methane productivity based on neural network models |
| title_fullStr | Prediction of coalbed methane productivity based on neural network models |
| title_full_unstemmed | Prediction of coalbed methane productivity based on neural network models |
| title_short | Prediction of coalbed methane productivity based on neural network models |
| title_sort | prediction of coalbed methane productivity based on neural network models |
| topic | lstm neural network bp neural network grey correlation analysis productivity prediction |
| url | http://xuebao.hpu.edu.cn/info/11196/95996.htm |
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